Statistical and Algorithmic Approaches to Bullet Matching

Heike Hofmann

Statistical and Algorithmic Approaches to Bullet Matching







Heike Hofmann, Alicia Carriquiry, Eric Hare, Haley Jeppson
Center for Statistics and Applications in Forensic Evidence
Iowa State University
June 8th, 2017

Problem Statement


Comparison Microscope

x3p format: ISO 25178-72:2017

Automated Matching Algorithm

Reference database

Results: Distributions of Known Matches and Known Non-Matches

Algorithm: Forest of 1000 trees

Automated Matching Algorithm: Front-End Web Application

https://isu-csafe.stat.iastate.edu/shiny/bulletr/

Testing the Model


Testing the Model (cont’d)

How much land do we need for a match?


Limitations

biggest limitation thus far is limited number of available 3D scan data for bullets:

in NIST ballistics database (Xiaoyu Alan Zhang, https://tsapps.nist.gov/NRBTD):

11 unique gun barrels is not yet enough to form a true reference distribution for known matches and non-matches…

…However, the structure of the database means that as soon as new data is available, the features and scores can be easily recomputed.

please contribute your experimental data!

Transparency & Reproducibility

Future work

  1. improve matching:
    • importance of parameter choices
    • expand on features, e.g. adapt toolmark scores to bullet lands
    • profile extraction (groove detection, area of extraction)
  2. investigate sources of (statistical) error: how much variability is introduced due to operator, lab, type of microscope? (ties into efforts by Martin Baiker from NFI and NIST)
  3. expand on applications: e.g. primer shearing marks?

Thank You

Special thanks to Alan Zheng at the National Institute of Standards and Technology for maintaining the NIST Ballistics Toolmark Research Database and providing many useful suggestions.

References

Bachrach, Benjamin, Anurag Jain, Sung Jung, and Robert D Koons. 2010. “A Statistical Validation of the Individuality and Repeatability of Striated Tool Marks: Screwdrivers and Tongue and Groove Pliers.” Journal of Forensic Sciences 55 (2): 348–57.

Biasotti, Alfred A. 1959. “A Statistical Study of the Individual Characteristics of Fired Bullets.” Journal of Forensic Sciences 4 (1): 34–50.

Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.

Clarkson, James A, and C Raymond Adams. 1933. “On Definitions of Bounded Variation for Functions of Two Variables.” Transactions of the American Mathematical Society 35 (4). JSTOR: 824–54.

Hamby, James E., David J. Brundage, and James W. Thorpe. 2009. “The Identification of Bullets Fired from 10 Consecutively Rifled 9mm Ruger Pistol Barrels: A Research Project Involving 507 Participants from 20 Countries.” AFTE Journal 41 (2): 99–110.

Vorburger, T.V., J.-F. Song, W. Chu, L. Ma, S.H. Bui, A. Zheng, and T.B. Renegar. 2011. “Applications of Cross-Correlation Functions.” Wear 271 (3–4): 529–33. doi:http://dx.doi.org/10.1016/j.wear.2010.03.030.